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Model|Simulation|Sensitivity analysis | Rank of parameters| Identifiability analisys | Parameter estimation | Optimal experimental design| Experiments
IVP Solvers | NLP Solvers

AMIGO implements the numerical steps incorporated in the iterative model identification loop in the right.

Simulation. The toolbox offers several dynamic simulation tasks to solve system dynamics under given values of model unknowns and given experimental schemes.

Sensitivity analysis and rank of parameters. It allows multi-experiment local and global sensitivity analysis for local and global model unknowns. Results may be analysed for each experiment or for the overall experimental scheme. A rank of the unknowns is stablished to asses their relative influence in the observables.

Parameter estimation. It allows for multi-experiment fitting of local (experiment dependent) and global unknowns. Several types of weighted least squares and log-likelihood functions, may be used depending on the available information about the experimental noise.

Identifiability analysis . Enables an evaluation of the possibility of assigning unique values to the parameters from a given set of experimental data or experimental scheme, subject to experimental noise through Fisher Information Matrix, Contour plots of the parameter estimation cost function and the robust analysis via a Monte-Carlo based approach.

Optimal experimental design via dynamic optimization. The purpose is to design sequential-parallel experimental schemes with the aim of maximizing data quality and quantity for the purpose of indetification. It allows the optimisation of the number and location of sampling times, stimulation profiles, initial conditions and experiment durations for one or more simultaneous experiments.

Numerical methods. AMIGO incorporates a suite of state of the art initial value problem (IVP) and non-linear optimization (NLP) methods so as to handle a large variety of problems.

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Home|Description|Documentation | Download| Contact